resnet50 distillation (#20)

#15

README.md

@@ -8,7 +8,8 @@ sparsity pruning depends on special algorithms and hardware to achieve accelerat
 Adlik pruning focuses on channel pruning and filter pruning, which can really reduce the number of parameters and
 flops. In terms of quantization, Adlik focuses on 8-bit quantization that is easier to accelerate on specific hardware.
 After testing, it is found that running a small batch of datasets can obtain a quantitative model with little loss of
-accuracy, so Adlik focuses on this method.
+accuracy, so Adlik focuses on this method. Knowledge distillation is another way to improve the performance of deep 
+learning algorithm. It is possible to compress the knowledge in the big model into a smaller model.
 
 The proposed framework mainly consists of two categories of algorithm components, i.e. pruner and quantizer. The
 pruner is mainly composed of five modules:core, scheduler, models, dataset and learner. The core module defines
@@ -35,6 +36,14 @@ The following table is the size of the above model files:
 | LeNet-5 | 1176KB | 499KB(59% pruned) | 120KB | 1154KB (pb) |
 | ResNet-50 | 99MB | 67MB(31.9% pruned) | 18MB | 138MB(pb) |
 
+Knowledge distillation is an effective way to imporve the performance of model.
+
+The following table shows the distillation result of ResNet-50 as the student network where ResNet-101 as the teacher network.
+
+| student model | ResNet-101 distilled | accuracy change |
+| ------------- | -------------------- | --------------- |
+| ResNet-50 | 77.14% | +0.97% |
+
 ## 1. Pruning and quantization principle
 
 ### 1.1 Filter pruning
@@ -63,6 +72,16 @@ quantization, only need to have inference model and very little calibration data
 of quantization is very small, and even some models will rise. Adlik only needs 100 sample images to complete the
 quantification of ResNet-50 in less than one minute.
 
+### 1.3 Knowledge Distillation
+
+Knowledge distillation is a compression technique by which the knowledge of a larger model(teacher) is transfered into
+a smaller one(student). During distillation, a student model learns from a teacher model to generalize well by raise
+the temperature of the final softmax of the teacher model as the soft set of targets. 
+
+![Distillation](imgs/distillation.png)
+
+Refer to the paper [Distilling the Knowledge in a Neural Network](https://arxiv.org/pdf/1503.02531.pdf)
+
 ## 2. Installation
 
 These instructions will help get Adlik optimizer up and running on your local machine.
@@ -102,7 +121,7 @@ rm -rf /tmp/openmpi
 #### 2.2.2 Install python package
 
 ```shell
-pip install tensorflow-gpu==2.1.0
+pip install tensorflow-gpu==2.3.0
 pip install horovod==0.19.1
 pip install mpi4py
 pip install networkx

doc/ResNet-50-Knowledge-Distillation.md

@@ -0,0 +1,46 @@
+# ResNet-50 Knowledge Distillation
+
+The following uses ResNet-101 on the ImageNet data set as teacher model to illustrate how to use the model optimizer to
+improve the preformance of ResNet-50 by knowledge distillation.
+
+## 1 Prepare data
+
+### 1.1 Generate training and test data sets
+
+You may follow the data preparation guide [here](https://github.com/tensorflow/models/tree/v1.13.0/research/inception)
+to download the full data set and convert it into TFRecord files. By default, when the script finishes, you will find
+1024 training files and 128 validation files in the DATA_DIR. The files will match the patterns train-?????-of-01024
+and validation-?????-of-00128, respectively.
+
+### 2 Train the teacher model
+
+Enter the examples directory and execute
+
+```shell
+cd examples
+horovodrun -np 8 -H localhost:8 python resnet_101_imagenet_train.py
+```
+
+After execution, the default checkpoint file will be generated in ./models_ckpt/resnet_101_imagenet, and the inference
+checkpoint file will be generated in ./models_eval_ckpt/resnet_101_imagenet. You can also modify the checkpoint_path
+and checkpoint_eval_path of the resnet_101_imagenet_train.py file to change the generated file path.
+
+### 3 Distill
+
+Enter the examples directory and execute
+
+```shell
+horovodrun -np 8 -H localhost:8 python resnet_50_imagenet_distill.py
+```
+
+After execution, the default checkpoint file will be generated in ./models_ckpt/resnet_50_imagenet_distill, and 
+the inference checkpoint file will be generated in ./models_eval_ckpt/resnet_50_imagenet_distill. You can also 
+modify the checkpoint_path and checkpoint_eval_path of the resnet_50_imagenet_distill.py file to change the generated 
+file path.
+
+> Note
+>  
+> > i. The model in the checkpoint_path is not the pure ResNet-50 model. It's the hybird of ResNet-50(student) and 
+> > ResNet-101(teacher)
+> >
+> > ii. The model in the checkpoint_eval_path is the distilled model, i.e. pure ResNet-50 model

docker/Dockerfile.cpu

@@ -3,7 +3,8 @@ FROM ubuntu:18.04
 RUN apt-get update && \
  apt-get install -y software-properties-common && \
  apt-get update -y && \
- apt-get install -y --no-install-recommends build-essential python3.6 python3.6-dev python3-distutils \
+ apt-get install -y --no-install-recommends --allow-downgrades --allow-change-held-packages \
+ build-essential python3.6 python3.6-dev python3-distutils \
  curl git openssh-client openssh-server && \
  mkdir -p /var/run/sshd && \
  mkdir -p /root/work && \
@@ -26,7 +27,7 @@ RUN mkdir /tmp/openmpi && \
  rm -rf /tmp/openmpi
 
 # Install Tensorflow and Horovod
-RUN pip install --no-cache-dir tensorflow==2.1.0
+RUN pip install --no-cache-dir tensorflow==2.3.0
 
 RUN HOROVOD_WITH_TENSORFLOW=1 pip install --no-cache-dir horovod==0.19.1
 

docker/Dockerfile.gpu

@@ -3,7 +3,8 @@ FROM nvidia/cuda:10.1-devel-ubuntu18.04
 RUN apt-get update && \
  apt-get install -y software-properties-common && \
  apt-get update -y && \
- apt-get install -y --no-install-recommends build-essential python3.6 python3.6-dev python3-distutils \
+ apt-get install -y --no-install-recommends --allow-downgrades --allow-change-held-packages \
+ build-essential python3.6 python3.6-dev python3-distutils \
  curl vim git openssh-client openssh-server \
  libcudnn7=7.6.5.32-1+cuda10.1 \
  libcudnn7-dev=7.6.5.32-1+cuda10.1 \
@@ -38,7 +39,7 @@ RUN mkdir /tmp/openmpi && \
  rm -rf /tmp/openmpi
 
 # Install Tensorflow and Horovod
-RUN pip install --no-cache-dir tensorflow-gpu==2.1.0
+RUN pip install --no-cache-dir tensorflow-gpu==2.3.0
 
 RUN HOROVOD_WITH_TENSORFLOW=1 pip install --no-cache-dir horovod==0.19.1
 

examples/resnet_101_imagenet_train.py

@@ -0,0 +1,37 @@
+# Copyright 2019 ZTE corporation. All Rights Reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+"""
+Train a ResNet_101 model on the ImageNet dataset
+"""
+import os
+# If you did not execute the setup.py, uncomment the following four lines
+# import sys
+# from os.path import abspath, join, dirname
+# sys.path.insert(0, join(abspath(dirname(__file__)), '../src'))
+# print(sys.path)
+
+from model_optimizer import prune_model # noqa: E402
+
+
+def _main():
+ base_dir = os.path.dirname(__file__)
+ request = {
+ "dataset": "imagenet",
+ "model_name": "resnet_101",
+ "data_dir": os.path.join(base_dir, "./data/imagenet"),
+ "batch_size": 128,
+ "batch_size_val": 100,
+ "learning_rate": 0.1,
+ "epochs": 120,
+ "checkpoint_path": os.path.join(base_dir, "./models_ckpt/resnet_101_imagenet"),
+ "checkpoint_save_period": 5, # save a checkpoint every 5 epoch
+ "checkpoint_eval_path": os.path.join(base_dir, "./models_eval_ckpt/resnet_101_imagenet"),
+ "scheduler": "train",
+ "classifier_activation": None # None or "softmax", default is softmax
+ }
+ prune_model(request)
+
+
+if __name__ == "__main__":
+ _main()

examples/resnet_50_imagenet_distill.py

@@ -0,0 +1,38 @@
+# Copyright 2020 ZTE corporation. All Rights Reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+"""
+Distill a ResNet_50 model from a trained ResNet_101 model on the ImageNet dataset
+"""
+import os
+# If you did not execute the setup.py, uncomment the following four lines
+# import sys
+# from os.path import abspath, join, dirname
+# sys.path.insert(0, join(abspath(dirname(__file__)), '../src'))
+# print(sys.path)
+
+from model_optimizer import prune_model # noqa: E402
+
+
+def _main():
+ base_dir = os.path.dirname(__file__)
+ request = {
+ "dataset": "imagenet",
+ "model_name": "resnet_50",
+ "data_dir": os.path.join(base_dir, "./data/imagenet"),
+ "batch_size": 256,
+ "batch_size_val": 100,
+ "learning_rate": 0.1,
+ "epochs": 90,
+ "checkpoint_path": os.path.join(base_dir, "./models_ckpt/resnet_50_imagenet_distill"),
+ "checkpoint_save_period": 5, # save a checkpoint every 5 epoch
+ "checkpoint_eval_path": os.path.join(base_dir, "./models_eval_ckpt/resnet_50_imagenet_distill"),
+ "scheduler": "distill",
+ "scheduler_file_name": "resnet_50_imagenet_0.3.yaml",
+ "classifier_activation": None # None or "softmax", default is softmax
+ }
+ prune_model(request)
+
+
+if __name__ == "__main__":
+ _main()

examples/resnet_50_imagenet_train.py

@@ -5,12 +5,13 @@
 Train a ResNet_50 model on the ImageNet dataset
 """
 import os
-# If you did not execute the setup.py, uncomment the following four lines
+
 # import sys
 # from os.path import abspath, join, dirname
 # sys.path.insert(0, join(abspath(dirname(__file__)), '../src'))
 # print(sys.path)
 
+
 from model_optimizer import prune_model # noqa: E402
 
 
@@ -27,7 +28,8 @@ def _main():
  "checkpoint_path": os.path.join(base_dir, "./models_ckpt/resnet_50_imagenet"),
  "checkpoint_save_period": 5, # save a checkpoint every 5 epoch
  "checkpoint_eval_path": os.path.join(base_dir, "./models_eval_ckpt/resnet_50_imagenet"),
- "scheduler": "train"
+ "scheduler": "train",
+ "classifier_activation": None # None or "softmax", default is softmax
  }
  prune_model(request)
 

setup.py

@@ -13,7 +13,7 @@
 
 _REQUIRED_PACKAGES = [
  'requests',
- 'tensorflow==2.1.0',
+ 'tensorflow==2.3.0',
  'jsonschema==3.1.1',
  'networkx==2.4',
  'mpi4py==3.0.3',
@@ -43,7 +43,7 @@ def get_dist(pkgname):
 
 
 if get_dist('tensorflow') is None and get_dist('tensorflow-gpu') is not None:
- _REQUIRED_PACKAGES.remove('tensorflow==2.1.0')
+ _REQUIRED_PACKAGES.remove('tensorflow==2.3.0')
 
 setup(
  name="model_optimizer",
@@ -60,7 +60,8 @@ def get_dist(pkgname):
  package_data={
  'model_optimizer': ['**/*.json',
  'pruner/scheduler/uniform_auto/*.yaml',
- 'pruner/scheduler/uniform_specified_layer/*.yaml']
+ 'pruner/scheduler/uniform_specified_layer/*.yaml',
+ 'pruner/scheduler/distill/*.yaml']
  },
 
 )

src/model_optimizer/pruner/dataset/cifar10.py

@@ -20,7 +20,7 @@ def __init__(self, config, is_training):
  :param is_training: whether to construct the training subset
  :return:
  """
- super(Cifar10Dataset, self).__init__(config, is_training)
+ super().__init__(config, is_training)
  if is_training:
  self.file_pattern = os.path.join(self.data_dir, 'train.tfrecords')
  self.batch_size = self.batch_size
@@ -32,6 +32,7 @@ def __init__(self, config, is_training):
  self.num_samples_of_train = 50000
  self.num_samples_of_val = 10000
 
+ # pylint: disable=no-value-for-parameter,unexpected-keyword-arg
  def parse_fn(self, example_serialized):
  """
  Parse features from the serialized data

src/model_optimizer/pruner/dataset/dataset_base.py

@@ -62,9 +62,10 @@ def num_samples(self):
  else:
  return self.num_samples_of_val
 
- def build(self):
+ def build(self, is_distill=False):
  """
  Build dataset
+ :param is_distill: is distilling or not
  :return: batch of a dataset
  """
  dataset = tf.data.Dataset.list_files(self.file_pattern, shuffle=True)
@@ -73,7 +74,10 @@ def build(self):
  dataset = dataset.interleave(self.dataset_fn, cycle_length=10, num_parallel_calls=tf.data.experimental.AUTOTUNE)
  if self.is_training:
  dataset = dataset.shuffle(buffer_size=self.buffer_size).repeat()
- dataset = dataset.map(self.parse_fn, num_parallel_calls=tf.data.experimental.AUTOTUNE)
+ if is_distill:
+ dataset = dataset.map(self.parse_fn_distill, num_parallel_calls=tf.data.experimental.AUTOTUNE)
+ else:
+ dataset = dataset.map(self.parse_fn, num_parallel_calls=tf.data.experimental.AUTOTUNE)
  return self.__build_batch(dataset)
 
  def __build_batch(self, dataset):

src/model_optimizer/pruner/dataset/imagenet.py

@@ -21,7 +21,7 @@ def __init__(self, config, is_training, num_shards=1, shard_index=0):
  :param is_training: whether to construct the training subset
  :return:
  """
- super(ImagenetDataset, self).__init__(config, is_training, num_shards, shard_index)
+ super().__init__(config, is_training, num_shards, shard_index)
  if is_training:
  self.file_pattern = os.path.join(self.data_dir, 'train-*-of-*')
  self.batch_size = self.batch_size
@@ -33,6 +33,7 @@ def __init__(self, config, is_training, num_shards=1, shard_index=0):
  self.num_samples_of_train = 1281167
  self.num_samples_of_val = 50000
 
+ # pylint: disable=no-value-for-parameter,unexpected-keyword-arg
  def parse_fn(self, example_serialized):
  """
  Parse features from the serialized data
@@ -77,3 +78,14 @@ def parse_fn(self, example_serialized):
  num_channels=3,
  is_training=self.is_training)
  return image, label
+
+ def parse_fn_distill(self, example_serialized):
+ """
+ Parse features from the serialized data for distillation
+ :param example_serialized: serialized data
+ :return: {image, label},{}
+ """
+ image, label = self.parse_fn(example_serialized)
+ inputs = {"image": image, "label": label}
+ targets = {}
+ return inputs, targets

src/model_optimizer/pruner/dataset/mnist.py

@@ -20,7 +20,7 @@ def __init__(self, config, is_training):
  :param is_training: whether to construct the training subset
  :return:
  """
- super(MnistDataset, self).__init__(config, is_training)
+ super().__init__(config, is_training)
  if is_training:
  self.file_pattern = os.path.join(self.data_dir, 'train.tfrecords')
  self.batch_size = self.batch_size
@@ -33,6 +33,7 @@ def __init__(self, config, is_training):
  self.num_samples_of_val = 10000
 
  # pylint: disable=R0201
+ # pylint: disable=no-value-for-parameter,unexpected-keyword-arg
  def parse_fn(self, example_serialized):
  """
  Parse features from the serialized data

src/model_optimizer/pruner/distill/__init__.py

@@ -0,0 +1,2 @@
+# Copyright 2021 ZTE corporation. All Rights Reserved.
+# SPDX-License-Identifier: Apache-2.0